AZ-400 Course Notes

This objective covers designing and implementing Azure Pipelines pipelines using templates, triggers, conditions, and parallel execution to achieve efficient and reliable build and release processes. Design and implement deployments using Azure Pipelines, leveraging strategies like canary, rolling, and blue-green deployments, along with deployment slots and lifecycle hooks to ensure controlled and reliable releases. Design and implement infrastructure as code using Bicep. This involves leveraging modularity, parameterized configurations, and governance processes to ensure repeatable and reliable deployments. Focus on declarative approaches and utilizing What-if previews for validation; however, understand that these previews do not guarantee successful execution or application functionality. Design and implement a testing strategy for pipelines that leverages test results for troubleshooting, traceability, and risk assessment. Design and implement a package management strategy leveraging Azure Artifacts feeds, focusing on versioning, promotion, and dependency consumption. Maintain pipelines by understanding and proactively addressing key performance indicators (KPIs) and operational considerations throughout the pipeline lifecycle. Configure and manage approvals and checks on protected resources to control access and ensure consistent deployments. Design and implement build and release pipelines by selecting appropriate agent infrastructure, considering factors like network access, hardware requirements, and security. Design self-service deployment environments by leveraging Azure Deployment Environments and their connection to governed catalogs. This approach ensures consistent, on-demand infrastructure while maintaining necessary governance controls. Implement feature flags to decouple feature exposure from code deployment. Feature flags are not authorization boundaries and require careful management to avoid complexity and security risks.

• A template centralizes reusable pipeline structure, while parameters are resolved at template expansion and variables can be evaluated at different runtime stages.

• When designing pipelines, consider the separation of concerns – use templates for structural reusability, parameters for template-time inputs, and conditions for controlling execution flow.

Analyze metrics from instrumentation to identify performance issues. Configure monitoring for a DevOps environment by implementing an instrumentation strategy that leverages Application Insights, distributed tracing, and appropriate alerting.

• Averages vs. Percentiles: Averages can hide tail latency; percentiles reveal the distribution of latency values.

• Prioritize understanding the limitations of averages when interpreting latency metrics.

Automate security and compliance scanning by leveraging GitHub Advanced Security's integrated scanning capabilities. This involves strategically deploying code, dependency, secret, and license analysis tools to address distinct risk categories and responding appropriately to identified vulnerabilities and threats. Design and implement authentication and authorization methods to secure Azure resource access for pipelines. Manage sensitive information in automation by leveraging Azure Key Vault for centralized secret storage, secure file handling, and robust rotation processes.

• CodeQL analyzes code for vulnerabilities, while Dependency Scanning identifies vulnerable components based on package information.

• Understand the limitations of each scanning type. Dependency alerts are not proof of an exploit.

Configure and manage Git repositories, particularly when dealing with large files and their associated history. This involves understanding how Git LFS interacts with existing repositories and how to manage changes effectively. Design and implement branching strategies for source code, focusing on the configuration and governance of merge quality gates and integration models. Configure source control permissions to ensure appropriate access levels and maintain security. Understand how permissions are inherited, explicitly set, and interact to determine effective access.

• Git LFS vs. Ordinary Git Blobs: Git LFS stores pointer files in Git while large file content is stored separately by the LFS service. Ordinary Git blobs store the entire binary content, increasing clone and history costs.

• Check scenarios involving large binary files. These often require a coordinated migration or history rewrite.

This objective covers designing and implementing processes and communications that establish traceability throughout the entire software delivery lifecycle, connecting code changes, builds, tests, releases, and work items. Design and implement appropriate metrics and queries for DevOps to drive actionable insights and avoid unintended consequences. Configure collaboration and communication by ensuring service hooks and communication integrations are designed to deliver the right information to the right people.

• Explicit workflow states provide clarity and governance, while free-form labels introduce ambiguity and hinder traceability.

• Focus on the interconnectedness of the delivery lifecycle. Don't treat any single link as definitive proof of success.